{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "data-and-model.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/wx-chevalier/AIDL-Series/blob/master/%E5%B7%A5%E5%85%B7%E5%AE%9E%E8%B7%B5/PyTorch/%E6%A8%A1%E5%9E%8B%E5%9F%BA%E7%A1%80/%E6%95%B0%E6%8D%AE%E4%B8%8E%E6%A8%A1%E5%9E%8B.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bOChJSNXtC9g",
        "colab_type": "text"
      },
      "source": [
        "# 数据与模型操作基础\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OLIxEDq6VhvZ",
        "colab_type": "text"
      },
      "source": [
        "<img src=\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/logo.png\" width=150>\n",
        "\n",
        "So far we've seen a varity of different models on different datasets for different tasks (regression/classification) and we're going to learn about even more algorithsm in subsequent lessons. But we've ignored a fundamental concept about data and modeling: quality and quantity. In a nutshe, a machine learning model consumes input data and produces predictions. The quality of the predictions directly corresponds to the quality and quantity of data you train the model with; garbage in, garbage out.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/nutshell.png\" width=500>\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLH7kzZl8wnf",
        "colab_type": "text"
      },
      "source": [
        "# Set Up"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qAE9BjMH8x4q",
        "colab_type": "text"
      },
      "source": [
        "We're going to go through all the concepts with concrete code examples. We'll first synthesize some data to train our models on. The task is to determine wether a tumor will be benign (harmless) or malignant (harmful) based on leukocyte (white blood cells) count and blood pressure. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0nzLDcVXJTx",
        "colab_type": "code",
        "outputId": "51d059af-d8a1-480c-8919-fbfbaee65836",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "# Load PyTorch library\n",
        "!pip3 install torch"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting torch\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7e/60/66415660aa46b23b5e1b72bc762e816736ce8d7260213e22365af51e8f9c/torch-1.0.0-cp36-cp36m-manylinux1_x86_64.whl (591.8MB)\n",
            "\u001b[K    100% |████████████████████████████████| 591.8MB 28kB/s \n",
            "tcmalloc: large alloc 1073750016 bytes == 0x6275e000 @  0x7fc423c362a4 0x591a07 0x5b5d56 0x502e9a 0x506859 0x502209 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x507641 0x502209 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x507641 0x504c28 0x502540 0x502f3d 0x507641\n",
            "\u001b[?25hInstalling collected packages: torch\n",
            "Successfully installed torch-1.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9uu2nngKDrW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from argparse import Namespace\n",
        "import collections\n",
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "import torch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gPHmsndLdUOH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set Numpy and PyTorch seeds\n",
        "def set_seeds(seed, cuda):\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    if cuda:\n",
        "        torch.cuda.manual_seed_all(seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-dXQiLlTIgz",
        "colab_type": "code",
        "outputId": "da0472de-6100-4301-ddfb-374da0ddbe2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Arguments\n",
        "args = Namespace(\n",
        "    seed=1234,\n",
        "    cuda=False,\n",
        "    shuffle=True,\n",
        "    data_file=\"tumors.csv\",\n",
        "    reduced_data_file=\"tumors_reduced.csv\",\n",
        "    train_size=0.75,\n",
        "    test_size=0.25,\n",
        "    num_hidden_units=100,\n",
        "    learning_rate=1e-3,\n",
        "    num_epochs=100,\n",
        ")\n",
        "\n",
        "# Set seeds\n",
        "set_seeds(seed=args.seed, cuda=args.cuda)\n",
        "\n",
        "# Check CUDA\n",
        "if not torch.cuda.is_available():\n",
        "    args.cuda = False\n",
        "args.device = torch.device(\"cuda\" if args.cuda else \"cpu\")\n",
        "print(\"Using CUDA: {}\".format(args.cuda))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using CUDA: False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RV2IddoZde-r",
        "colab_type": "text"
      },
      "source": [
        "# Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wDazzQdaoy2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re\n",
        "import urllib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbsXoFVgdh6K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Upload data from GitHub to notebook's local drive\n",
        "url = \"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/data/tumors.csv\"\n",
        "response = urllib.request.urlopen(url)\n",
        "html = response.read()\n",
        "with open(args.data_file, 'wb') as fp:\n",
        "    fp.write(html)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y6LNWmoidh8q",
        "colab_type": "code",
        "outputId": "36ad0be8-b618-409f-e30c-cc2ff3bc1ddf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Raw data\n",
        "df = pd.read_csv(args.data_file, header=0)\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>leukocyte_count</th>\n",
              "      <th>blood_pressure</th>\n",
              "      <th>tumor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13.472969</td>\n",
              "      <td>15.250393</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.805510</td>\n",
              "      <td>14.109676</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.834053</td>\n",
              "      <td>15.793920</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.572811</td>\n",
              "      <td>17.873286</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.633667</td>\n",
              "      <td>16.598559</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   leukocyte_count  blood_pressure  tumor\n",
              "0        13.472969       15.250393      1\n",
              "1        10.805510       14.109676      1\n",
              "2        13.834053       15.793920      1\n",
              "3         9.572811       17.873286      1\n",
              "4         7.633667       16.598559      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVo6CuZLC3h2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_tumors(df):\n",
        "    i = 0; colors=['r', 'b']\n",
        "    for name, group in df.groupby(\"tumor\"):\n",
        "        plt.scatter(group.leukocyte_count, group.blood_pressure, edgecolors='k',\n",
        "                   color=colors[i]); i += 1\n",
        "    plt.xlabel('leukocyte count')\n",
        "    plt.ylabel('blood pressure')\n",
        "    plt.legend(['0 - benign', '1 - malignant'], loc=\"upper right\")\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXFUmnfte6z6",
        "colab_type": "code",
        "outputId": "66a7d2c5-e3be-4625-c8a8-8c0f7dca059d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "source": [
        "# Plot data\n",
        "plot_tumors(df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1PR1Pjzk5eSc1NZ3wl0ymKDqfxm/z5h7s3DmKBQvwmQinJk0VFXXHZMoEjDjL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\nBAKBIET4/y8q7wsoT6NxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd96e54ac18>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "237OzHqlNQ-D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert to PyTorch tensors\n",
        "X = df.as_matrix(columns=['leukocyte_count', 'blood_pressure'])\n",
        "y = df.as_matrix(columns=['tumor'])\n",
        "X = torch.from_numpy(X).float()\n",
        "y = torch.from_numpy(y.ravel()).long()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pahDv9WLD2S",
        "colab_type": "code",
        "outputId": "3ca326a1-631e-490e-bd1c-bb464fa89504",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Shuffle data\n",
        "shuffle_indicies = torch.LongTensor(random.sample(range(0, len(X)), len(X)))\n",
        "X = X[shuffle_indicies]\n",
        "y = y[shuffle_indicies]\n",
        "\n",
        "# Split datasets\n",
        "test_start_idx = int(len(X) * args.train_size)\n",
        "X_train = X[:test_start_idx] \n",
        "y_train = y[:test_start_idx] \n",
        "X_test = X[test_start_idx:] \n",
        "y_test = y[test_start_idx:]\n",
        "print(\"We have %i train samples and %i test samples.\" % (len(X_train), len(X_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We have 750 train samples and 250 test samples.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "owLnzReJJdpj",
        "colab_type": "text"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zlPe1lXEJfcA",
        "colab_type": "text"
      },
      "source": [
        "Let's fit a model on this synthetic data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0WhYfDOjJdIV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTtsFHZY_G45",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Multilayer Perceptron \n",
        "class MLP(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
        "        super(MLP, self).__init__()\n",
        "        self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
        "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
        "\n",
        "    def forward(self, x_in, apply_softmax=False):\n",
        "        a_1 = F.relu(self.fc1(x_in)) # activaton function added!\n",
        "        y_pred = self.fc2(a_1)\n",
        "\n",
        "        if apply_softmax:\n",
        "            y_pred = F.softmax(y_pred, dim=1)\n",
        "\n",
        "        return y_pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1kXlfHpPJ5Vq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Initialize model\n",
        "model = MLP(input_dim=len(df.columns)-1, \n",
        "            hidden_dim=args.num_hidden_units, \n",
        "            output_dim=len(set(df.tumor)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ncxbef0yJ6pD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Optimization\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=args.learning_rate) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srlaBr8oiftE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Accuracy\n",
        "def get_accuracy(y_pred, y_target):\n",
        "    n_correct = torch.eq(y_pred, y_target).sum().item()\n",
        "    accuracy = n_correct / len(y_pred) * 100\n",
        "    return accuracy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mjg4u-zCK90q",
        "colab_type": "code",
        "outputId": "3342fa5b-ed8b-4291-ea92-4566eae43807",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "# Training\n",
        "for t in range(args.num_epochs):\n",
        "    # Forward pass\n",
        "    y_pred = model(X_train)\n",
        "    \n",
        "    # Accuracy\n",
        "    _, predictions = y_pred.max(dim=1)\n",
        "    accuracy = get_accuracy(y_pred=predictions.long(), y_target=y_train)\n",
        "\n",
        "    # Loss\n",
        "    loss = loss_fn(y_pred, y_train)\n",
        "    \n",
        "    # Verbose\n",
        "    if t%20==0: \n",
        "        print (\"epoch: {0:02d} | loss: {1:.4f} | accuracy: {2:.1f}%\".format(\n",
        "            t, loss, accuracy))\n",
        "\n",
        "    # Zero all gradients\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # Backward pass\n",
        "    loss.backward()\n",
        "\n",
        "    # Update weights\n",
        "    optimizer.step()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch: 00 | loss: 4.4963 | accuracy: 39.5%\n",
            "epoch: 20 | loss: 0.9152 | accuracy: 60.5%\n",
            "epoch: 40 | loss: 0.3904 | accuracy: 94.4%\n",
            "epoch: 60 | loss: 0.2871 | accuracy: 97.9%\n",
            "epoch: 80 | loss: 0.1893 | accuracy: 98.4%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHCvuSEaK-2x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predictions\n",
        "_, pred_train = model(X_train, apply_softmax=True).max(dim=1)\n",
        "_, pred_test = model(X_test, apply_softmax=True).max(dim=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5whE6K0rOmGN",
        "colab_type": "code",
        "outputId": "eccb440a-b73b-4f9a-8784-74a0f6462b39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Train and test accuracies\n",
        "train_acc = get_accuracy(y_pred=pred_train, y_target=y_train)\n",
        "test_acc = get_accuracy(y_pred=pred_test, y_target=y_test)\n",
        "print (\"train acc: {0:.1f}%, test acc: {1:.1f}%\".format(train_acc, test_acc))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train acc: 98.7%, test acc: 98.8%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzFb90SJOmI2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Visualization\n",
        "def plot_multiclass_decision_boundary(model, X, y):\n",
        "    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
        "    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
        "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101), np.linspace(y_min, y_max, 101))\n",
        "    cmap = plt.cm.Spectral\n",
        "    \n",
        "    X_test = torch.from_numpy(np.c_[xx.ravel(), yy.ravel()]).float()\n",
        "    y_pred = model(X_test, apply_softmax=True)\n",
        "    _, y_pred = y_pred.max(dim=1)\n",
        "    y_pred = y_pred.reshape(xx.shape)\n",
        "    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
        "    plt.xlim(xx.min(), xx.max())\n",
        "    plt.ylim(yy.min(), yy.max())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ViwfNFOYRDkm",
        "colab_type": "text"
      },
      "source": [
        "We're going to plot a white point, which we know belongs to the malignant tumor class. Our well trained model here would accurate predict that it is indeed a malignant tumor!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_oEf6XRmOsJE",
        "colab_type": "code",
        "outputId": "5e86ded5-76b8-426e-dc17-879bb1b5795f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "source": [
        "# Visualize the decision boundary\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n",
        "plt.scatter(np.mean(df.leukocyte_count), np.mean(df.blood_pressure), s=200, \n",
        "            c='b', edgecolor='w', linewidth=2)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n",
        "plt.scatter(np.mean(df.leukocyte_count), np.mean(df.blood_pressure), s=200, \n",
        "            c='b', edgecolor='w', linewidth=2)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1Pmdg/2pxI0dCnYJXguBEYN9i2Ixzn2p6Axf/x82DDkUg2FfkXhepdrmjxle2yYw/MtK2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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd96bc42978>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o231eJaQPi5E",
        "colab_type": "text"
      },
      "source": [
        "Great! We received great performances on both our train and test data splits. We're going to use this dataset to show the important of data quality and quantity."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZ3rnGH8PtBu",
        "colab_type": "text"
      },
      "source": [
        "# Data Quality and Quantity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONRP3WQgR3zc",
        "colab_type": "text"
      },
      "source": [
        "Let's remove some training data near the decision boundary and see how robust the model is now."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sxd2S63EYtxt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Upload data from GitHub to notebook's local drive\n",
        "url = \"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/data/tumors_reduced.csv\"\n",
        "response = urllib.request.urlopen(url)\n",
        "html = response.read()\n",
        "with open(args.reduced_data_file, 'wb') as fp:\n",
        "    fp.write(html)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sU69PjH3Z4bm",
        "colab_type": "code",
        "outputId": "309a83f6-a3fc-4936-eff5-bc82df3d82b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Raw reduced data\n",
        "df_reduced = pd.read_csv(args.reduced_data_file, header=0)\n",
        "df_reduced.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>leukocyte_count</th>\n",
              "      <th>blood_pressure</th>\n",
              "      <th>tumor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13.472969</td>\n",
              "      <td>15.250393</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.805510</td>\n",
              "      <td>14.109676</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.834053</td>\n",
              "      <td>15.793920</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.572811</td>\n",
              "      <td>17.873286</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.633667</td>\n",
              "      <td>16.598559</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   leukocyte_count  blood_pressure  tumor\n",
              "0        13.472969       15.250393      1\n",
              "1        10.805510       14.109676      1\n",
              "2        13.834053       15.793920      1\n",
              "3         9.572811       17.873286      1\n",
              "4         7.633667       16.598559      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1OwgEJSsZ4g5",
        "colab_type": "code",
        "outputId": "5104288e-6b94-47a6-d5f1-1ae72b4c341d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "source": [
        "# Plot data\n",
        "plot_tumors(df_reduced)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nrAWBYE+TcvncW5pUczR0BUUA7x461Geh66jdH7XZ+K3Fwp0Wi+jE1QoIoX2R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\nUQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd96bc429b0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9xlQme0beTY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert to PyTorch tensors\n",
        "X = df_reduced.as_matrix(columns=['leukocyte_count', 'blood_pressure'])\n",
        "y = df_reduced.as_matrix(columns=['tumor'])\n",
        "X = torch.from_numpy(X).float()\n",
        "y = torch.from_numpy(y.ravel()).long()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RerzDWJQbeVz",
        "colab_type": "code",
        "outputId": "6518a4b9-388b-4b6c-abeb-cc7299465294",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Shuffle data\n",
        "shuffle_indicies = torch.LongTensor(random.sample(range(0, len(X)), len(X)))\n",
        "X = X[shuffle_indicies]\n",
        "y = y[shuffle_indicies]\n",
        "\n",
        "# Split datasets\n",
        "test_start_idx = int(len(X) * args.train_size)\n",
        "X_train = X[:test_start_idx] \n",
        "y_train = y[:test_start_idx] \n",
        "X_test = X[test_start_idx:] \n",
        "y_test = y[test_start_idx:]\n",
        "print(\"We have %i train samples and %i test samples.\" % (len(X_train), len(X_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "We have 540 train samples and 180 test samples.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCZ7yDl1OsdU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Initialize model\n",
        "model = MLP(input_dim=len(df_reduced.columns)-1, \n",
        "            hidden_dim=args.num_hidden_units, \n",
        "            output_dim=len(set(df_reduced.tumor)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-IZ4YOKtSCRk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Optimization\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=args.learning_rate) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NBWLKDISDj8",
        "colab_type": "code",
        "outputId": "4aae7acd-69c3-4e73-e3b1-0ce0e90c4fb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "# Training\n",
        "for t in range(args.num_epochs):\n",
        "    # Forward pass\n",
        "    y_pred = model(X_train)\n",
        "    \n",
        "    # Accuracy\n",
        "    _, predictions = y_pred.max(dim=1)\n",
        "    accuracy = get_accuracy(y_pred=predictions.long(), y_target=y_train)\n",
        "\n",
        "    # Loss\n",
        "    loss = loss_fn(y_pred, y_train)\n",
        "    \n",
        "    # Verbose\n",
        "    if t%20==0: \n",
        "        print (\"epoch: {0} | loss: {1:.4f} | accuracy: {2:.1f}%\".format(t, loss, accuracy))\n",
        "\n",
        "    # Zero all gradients\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # Backward pass\n",
        "    loss.backward()\n",
        "\n",
        "    # Update weights\n",
        "    optimizer.step()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch: 0 | loss: 0.9259 | accuracy: 52.6%\n",
            "epoch: 20 | loss: 0.2902 | accuracy: 100.0%\n",
            "epoch: 40 | loss: 0.1359 | accuracy: 100.0%\n",
            "epoch: 60 | loss: 0.0763 | accuracy: 100.0%\n",
            "epoch: 80 | loss: 0.0519 | accuracy: 100.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGWbZlhUSFOz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predictions\n",
        "_, pred_train = model(X_train, apply_softmax=True).max(dim=1)\n",
        "_, pred_test = model(X_test, apply_softmax=True).max(dim=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gz2Sh4JpSFT9",
        "colab_type": "code",
        "outputId": "d2d47b18-033a-450c-9433-2331c3ee0819",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Train and test accuracies\n",
        "train_acc = get_accuracy(y_pred=pred_train, y_target=y_train)\n",
        "test_acc = get_accuracy(y_pred=pred_test, y_target=y_test)\n",
        "print (\"train acc: {0:.1f}%, test acc: {1:.1f}%\".format(train_acc, test_acc))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train acc: 100.0%, test acc: 100.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DmTCz8OnSFRn",
        "colab_type": "code",
        "outputId": "b00f822b-3119-432d-f6bb-db89e82f4bbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "source": [
        "# Visualize the decision boundary\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n",
        "plt.scatter(np.mean(df.leukocyte_count), np.mean(df.blood_pressure), s=200, \n",
        "            c='b', edgecolor='w', linewidth=2)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n",
        "plt.scatter(np.mean(df.leukocyte_count), np.mean(df.blood_pressure), s=200, \n",
        "            c='b', edgecolor='w', linewidth=2)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4G7xj82RBqehfxlcVrTosokWLBza/teklOJZn4T/m2/IxNK06priSr8A57NCt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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fd96bc094e0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kdP98xnlbvVn",
        "colab_type": "text"
      },
      "source": [
        "This is a very scary but highly realistic scenario. Based on our reduced synthetic dataset, we have achieved a model that generalized really well on the test data. But when we ask for the prediction for the same white point earlier (which we known is a tumor), the prediction is now a benign tumor. We would have completely missed the tumor.\n",
        "\n",
        "**MODELS ARE NOT CRYSTAL BALLS** \n",
        "It's so important that before any machine learning, we really look at our data and ask ourselves if it is truly representative for the task we want solve. The model itself may fit really well and generalize well on your data but if the data is of poor quality to begin with, the model cannot be trusted."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWzAC39adTwk",
        "colab_type": "text"
      },
      "source": [
        "# Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cR45QpjQdY6N",
        "colab_type": "text"
      },
      "source": [
        "Once you are confident that you data is of good quality and quantity, you can findally start thinking about modeling. The type of model you choose depends on many factors, including the task, type of data, complexity required, etc.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/models1.png\" width=550>\n",
        "\n",
        "So once you figure out what type of model your task needs, start with simple models and then slowly add complexity. You don’t want to start with neural networks right away because that may not be right model for your data and task. Striking this balance in model complexity is one of the key tasks of your data scientists. **simple models → complex models**"
      ]
    }
  ]
}